1. Field of the Invention
The present invention relates to a novel method for configuring a neural network and a neural network diagnosis/control system which uses the neural network.
2. Description of the Related Art
In diagnosis of electric power equipment, various measurements for various types of equipment are analyzed by experts of the arts to determine whether the equipment is normal or not. If it is determined to be abnormal, a most probable cause is inferred. For example, in JP-A-2-297074, a diagnosis method using a neural network is proposed. In the method, past status of various types of equipment are previously learned by a neural network having a multi-layer structure. When new status of the equipment is input, whether or not equipment is normal is determined based on the learn result. If equipment is determined to be abnormal, a cause therefor is inferred.
In JP-A-2-272326, detailed description is made on mechanical vibration, acoustic vibration and electric oscillation of a rotary machine.
FIG. 1 shows a conventional neural network. Any number (one in the illustrated example) of hidden layers are provided between an input layer and an output layer. A set of input data X.sub.1, X.sub.2, X.sub.3, . . . , X.sub.n is input to the input layer. In FIG. 1, "1" is also always supplied for adjusting the output from neurons. Products of those inputs and coupling weights W.sub.1, W.sub.2, W.sub.3, . . . , W.sub.n, W.sub.n+1 are inputs U.sub.1 to neuron Ne. An output from the neuron is V.sub.1. U.sub.1 and V.sub.1 are calculated by equations (1) and (2). ##EQU1##
Similar equations are applied to the inputs and the outputs of other neurons of the hidden layers and the output layers. Only one Na of the neurons of each hidden layer is coupled with "1" for adjusting the output of a neuron connected thereto which the output of the connected neuron is equal to "1" to provide a predetermined coupling weight.
In JP-A-2-100757, a learning method of a parallel neural network is proposed. It is stated therein that a back propagation method is becoming effective as a learning method. A conventional learning method has disadvantages in that the learning does not proceed once it falls in a local minimum and a precision of learning sharply decreases when the amount of data to be learned is large. In the proposed method, more than one neural network is connected in parallel to serially learn in order to improve the efficiency and the precision of the learning.
Thus, in the conventional neural network, an operation of learning is essential. The learning means includes coupling weights among the neurons that are determined through iterative calculations so that a calculated value approximated to data called teacher data which is predetermined for input data is output from the output layer when the input data is applied. For example, it is the learning to gradually modify the coupling weights through the iterative calculations, to reduce an error which is defined as 0.5 times of a square sum of a difference between the teacher data and the calculated output. If the learning proceeds well, the error decreases, but in some cases the error does not decrease and the calculated output is not attained with sufficient precision even after a large number of times of learning. Such a case is referred to as the neural network having fallen in a local minimum. In such a case, it is not possible to get out of the local minimum whatever number of times of the learning is increased and appropriate coupling weights are not determined.
Even if the calculated output converges to a desired value, a large number of times of learning requires a very long time (for example, several hours or more) depending on the scale of the neural network and the teacher data.
A method for reducing the learning time is disclosed in JP-A-3-286388, JP-A-3-201158 and JP-A-3-201160. In JP-A-3-286388, the coupling weight W(k) is so modified that it is proportional to a difference between a teacher signal U(k) and a mathematical equation model output Z(k). In JP-A-3-201158 and JP-A-3-201160, the variance of a coupling weight is modified in accordance with a learning constant and a learning variable. However, there still remains the problem that the learning time is long.
In the conventional neural network, the optimum number of hidden layers and the number of neurons are determined by a trial and error method. It is inefficient and the improvement thereof has been desired.
The neural network used for the diagnostics of the abnormal state of equipment usually includes an input layer, hidden layers and an output layer, and the numbers of neurons of the respective layers are huge and hence a long time is required for the learning. The numbers of neurons of the input layer and the output layer are determined by the types of sensors used and the types of trouble to be detected. However, it is difficult to previously determine the number of neurons of each of the hidden layers, and it is determined in the trial and error method in any case. For example, in "Neural Networks", Vol. 4, No. 1, 1991, pages 61-66, a relatively small scale neural network is disclosed in which a total error of the neural network is calculated after every 100 times of learning are completed for a hidden layer starting from the hidden layer with one neuron. If the error does not decrease by more than 1%, the number of neurons of the hidden layer is increased by one, and if the error is reduced by more than 1%, another 100 times of learning are made. In this method, since the number of neurons of the hidden layer increases remarkably in certain cases, a method for reducing the number of neurons of the hidden layer is also introduced. In the reduction method, once the neural network converges, the number of neurons of the hidden layer is reduced by one and the learning is conducted. If the neural network converges, the above is repeated. This process is repeated until the neural network no longer converges. In this method, however, since the learning time is inherently long, a long time is required to construct the neural network.
Further, because of rapid progress in various technical fields, it may be desired to modify the neural network if a new sensor which is effective for modification, expansion or detection of trouble is developed after the completion of the learning of the neural network. In such a case, re-learning is required for the previously learned inputs and a new input. However, when the large-scaled neural network is used, it is a great loss of time to repeat the learning starting from the hidden layer with one neuron to the final stage while increasing the number of neurons one at a time.